Merge pull request #1 from SturgeonMi/Azure-SQL-database-patch-1

Update how-to-identity-based-data-access.md

Author: SturgeonMi

articles/machine-learning/how-to-identity-based-data-access.md

@@ -142,6 +142,21 @@ adls2_dstore = Datastore.register_azure_data_lake_gen2(workspace=ws,
                                                        account_name='myadls2')
 ```
 
+### Azure SQL database
+For an Azure SQL database, use [register_azure_sql_database()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.datastore.datastore?view=azure-ml-py#register-azure-sql-database-workspace--datastore-name--server-name--database-name--tenant-id-none--client-id-none--client-secret-none--resource-url-none--authority-url-none--endpoint-none--overwrite-false--username-none--password-none--subscription-id-none--resource-group-none--grant-workspace-access-false----kwargs-) to register a datastore that connects to an Azure SQL database storage.
+
+The following code creates and registers the `credentialless_sqldb` datastore to the `ws` workspace and assigns it to the variable, `sqldb_dstore`. This datastore accesses the database `mydb` in the `myserver` SQL DB server.  
+
+```python
+# createn sqldatabase datastore without credentials
+                                                       
+sqldb_dstore = Datastore.register_azure_sql_database(workspace=ws,
+                                                       datastore_name='credentialless_sqldb',
+                                                       server_name='myserver',
+                                                       database_name='mydb')                                                       
+                                                   
+```
+
 ## Use data in storage
 
 [Azure Machine Learning datasets](how-to-create-register-datasets.md) are the recommended way to interact with your data in storage with Azure Machine Learning.